<?xml version="1.0"?>
<doc>
    <assembly>
        <name>Studio.Algorithm.SVM</name>
    </assembly>
    <members>
        <member name="T:Studio.Algorithm.SVM.Prediction">
            <summary>
            Class containing the routines to perform class membership prediction using a trained SVM.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Prediction.Predict(Studio.Algorithm.SVM.Problem,System.String,Studio.Algorithm.SVM.Model,System.Boolean)">
            <summary>
            Predicts the class memberships of all the vectors in the problem.
            </summary>
            <param name="problem">The SVM Problem to solve</param>
            <param name="outputFile">File for result output</param>
            <param name="model">The Model to use</param>
            <param name="predict_probability">Whether to output a distribution over the classes</param>
            <returns>Percentage correctly labelled</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Prediction.Predict(Studio.Algorithm.SVM.Model,Studio.Algorithm.SVM.Node[])">
            <summary>
            Predict the class for a single input vector.
            </summary>
            <param name="model">The Model to use for prediction</param>
            <param name="x">The vector for which to predict class</param>
            <returns>The result</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Prediction.PredictProbability(Studio.Algorithm.SVM.Model,Studio.Algorithm.SVM.Node[])">
            <summary>
            Predicts a class distribution for the single input vector.
            </summary>
            <param name="model">Model to use for prediction</param>
            <param name="x">The vector for which to predict the class distribution</param>
            <returns>A probability distribtion over classes</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Prediction.Predict(System.String[])">
            <summary>
            Legacy method, provided to allow usage as though this were the command line version of libsvm.
            </summary>
            <param name="args">Standard arguments passed to the svm_predict exectutable.  See libsvm documentation for details.</param>
        </member>
        <member name="T:Studio.Algorithm.SVM.Scaling">
            <summary>
            Deals with the scaling of Problems so they have uniform ranges across all dimensions in order to
            result in better SVM performance.
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.Scaling.DEFAULT_LOWER_BOUND">
            <summary>
            Default lower bound for scaling (-1).
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.Scaling.DEFAULT_UPPER_BOUND">
            <summary>
            Default upper bound for scaling (1).
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Scaling.DetermineRange(Studio.Algorithm.SVM.Problem)">
            <summary>
            Determines the Range transform for the provided problem.  Uses the default lower and upper bounds.
            </summary>
            <param name="prob">The Problem to analyze</param>
            <returns>The Range transform for the problem</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Scaling.DetermineRangeTransform(Studio.Algorithm.SVM.Problem,System.Double,System.Double)">
            <summary>
            Determines the Range transform for the provided problem.
            </summary>
            <param name="prob">The Problem to analyze</param>
            <param name="lowerBound">The lower bound for scaling</param>
            <param name="upperBound">The upper bound for scaling</param>
            <returns>The Range transform for the problem</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Scaling.Scale(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.IRangeTransform)">
            <summary>
            Scales a problem using the provided range.  This will not affect the parameter.
            </summary>
            <param name="prob">The problem to scale</param>
            <param name="range">The Range transform to use in scaling</param>
            <returns>The Scaled problem</returns>
        </member>
        <member name="T:Studio.Algorithm.SVM.GaussianTransform">
            <summary>
            A transform which learns the mean and variance of a sample set and uses these to transform new data
            so that it has zero mean and unit variance.
            </summary>
        </member>
        <member name="T:Studio.Algorithm.SVM.IRangeTransform">
            <summary>
            Interface implemented by range transforms.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.IRangeTransform.Transform(System.Double,System.Int32)">
            <summary>
            Transform the input value using the transform stored for the provided index.
            </summary>
            <param name="input">Input value</param>
            <param name="index">Index of the transform to use</param>
            <returns>The transformed value</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.IRangeTransform.Transform(Studio.Algorithm.SVM.Node[])">
            <summary>
            Transforms the input array.
            </summary>
            <param name="input">The array to transform</param>
            <returns>The transformed array</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.#ctor(System.Int32)">
            <summary>
            Constructor.
            </summary>
            <param name="maxIndex">The maximum index of the vectors to be transformed</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.Add(Studio.Algorithm.SVM.Node[])">
            <summary>
            Adds a sample to the data.  No computation is performed.  The maximum index of the
            sample must be less than MaxIndex.
            </summary>
            <param name="sample">The sample to add</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.ComputeStatistics">
            <summary>
            Computes the statistics for the samples which have been obtained so far.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.Write(System.IO.Stream,Studio.Algorithm.SVM.GaussianTransform)">
            <summary>
            Saves the transform to the disk.  The samples are not stored, only the 
            statistics.
            </summary>
            <param name="stream">The destination stream</param>
            <param name="transform">The transform</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.Read(System.IO.Stream)">
            <summary>
            Reads a GaussianTransform from the provided stream.
            </summary>
            <param name="stream">The source stream</param>
            <returns>The transform</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.Write(System.String,Studio.Algorithm.SVM.GaussianTransform)">
            <summary>
            Saves the transform to the disk.  The samples are not stored, only the 
            statistics.
            </summary>
            <param name="filename">The destination filename</param>
            <param name="transform">The transform</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.Read(System.String)">
            <summary>
            Reads a GaussianTransform from the provided stream.
            </summary>
            <param name="filename">The source filename</param>
            <returns>The transform</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.Transform(System.Double,System.Int32)">
            <summary>
            Transform the input value using the transform stored for the provided index.
            <see cref="M:Studio.Algorithm.SVM.GaussianTransform.ComputeStatistics"/> must be called first, or the transform must
            have been read from the disk.
            </summary>
            <param name="input">Input value</param>
            <param name="index">Index of the transform to use</param>
            <returns>The transformed value</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.GaussianTransform.Transform(Studio.Algorithm.SVM.Node[])">
            <summary>
            Transforms the input array.  <see cref="M:Studio.Algorithm.SVM.GaussianTransform.ComputeStatistics"/> must be called 
            first, or the transform must have been read from the disk.
            </summary>
            <param name="input">The array to transform</param>
            <returns>The transformed array</returns>
        </member>
        <member name="T:Studio.Algorithm.SVM.RangeTransform">
            <summary>
            Class which encapsulates a range transformation.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.RangeTransform.#ctor(System.Double[],System.Double[],System.Double,System.Double)">
            <summary>
            Constructor.
            </summary>
            <param name="minValues">The minimum values in each dimension.</param>
            <param name="maxValues">The maximum values in each dimension.</param>
            <param name="lowerBound">The desired lower bound for all dimensions.</param>
            <param name="upperBound">The desired upper bound for all dimensions.</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.RangeTransform.Transform(Studio.Algorithm.SVM.Node[])">
            <summary>
            Transforms the input array based upon the values provided.
            </summary>
            <param name="input">The input array</param>
            <returns>A scaled array</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.RangeTransform.Transform(System.Double,System.Int32)">
            <summary>
            Transforms this an input value using the scaling transform for the provided dimension.
            </summary>
            <param name="input">The input value to transform</param>
            <param name="index">The dimension whose scaling transform should be used</param>
            <returns>The scaled value</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.RangeTransform.Write(System.IO.Stream,Studio.Algorithm.SVM.RangeTransform)">
            <summary>
            Writes this Range transform to a stream.
            </summary>
            <param name="stream">The stream to write to</param>
            <param name="r">The range to write</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.RangeTransform.Write(System.String,Studio.Algorithm.SVM.RangeTransform)">
            <summary>
            Writes this Range transform to a file.    This will overwrite any previous data in the file.
            </summary>
            <param name="outputFile">The file to write to</param>
            <param name="r">The Range to write</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.RangeTransform.Read(System.String)">
            <summary>
            Reads a Range transform from a file.
            </summary>
            <param name="inputFile">The file to read from</param>
            <returns>The Range transform</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.RangeTransform.Read(System.IO.Stream)">
            <summary>
            Reads a Range transform from a stream.
            </summary>
            <param name="stream">The stream to read from</param>
            <returns>The Range transform</returns>
        </member>
        <member name="T:Studio.Algorithm.SVM.Training">
            <summary>
            Class containing the routines to train SVM models.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Training.Train(System.String[])">
            <summary>
            Legacy.  Allows use as if this was svm_train.  See libsvm documentation for details on which arguments to pass.
            </summary>
            <param name="args"></param>
        </member>
        <member name="M:Studio.Algorithm.SVM.Training.PerformCrossValidation(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Parameter,System.Int32)">
            <summary>
            Performs cross validation.
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to test</param>
            <param name="nrfold">The number of cross validations to use</param>
            <returns>The cross validation score</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Training.Train(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Parameter)">
            <summary>
            Trains a model using the provided training data and parameters.
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use</param>
            <returns>A trained SVM Model</returns>
        </member>
        <member name="T:Studio.Algorithm.SVM.Node">
            <summary>
            Encapsulates a node in a Problem vector, with an index and a value (for more efficient representation
            of sparse data.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Node.#ctor">
            <summary>
            Default Constructor.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Node.#ctor(System.Int32,System.Double)">
            <summary>
            Constructor.
            </summary>
            <param name="index">The index of the value.</param>
            <param name="value">The value to store.</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.Node.ToString">
            <summary>
            String representation of this Node as {index}:{value}.
            </summary>
            <returns>{index}:{value}</returns>
        </member>
        <member name="P:Studio.Algorithm.SVM.Node.Index">
            <summary>
            Index of this Node.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Node.Value">
            <summary>
            Value at Index.
            </summary>
        </member>
        <member name="T:Studio.Algorithm.SVM.ParameterSelection">
            <summary>
            This class contains routines which perform parameter selection for a model which uses C-SVC and
            an RBF kernel.
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.ParameterSelection.NFOLD">
            <summary>
            Default number of times to divide the data.
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.ParameterSelection.MIN_C">
            <summary>
            Default minimum power of 2 for the C value (-5)
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.ParameterSelection.MAX_C">
            <summary>
            Default maximum power of 2 for the C value (15)
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.ParameterSelection.C_STEP">
            <summary>
            Default power iteration step for the C value (2)
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.ParameterSelection.MIN_G">
            <summary>
            Default minimum power of 2 for the Gamma value (-15)
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.ParameterSelection.MAX_G">
            <summary>
            Default maximum power of 2 for the Gamma Value (3)
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.ParameterSelection.G_STEP">
            <summary>
            Default power iteration step for the Gamma value (2)
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.ParameterSelection.GetList(System.Double,System.Double,System.Double)">
            <summary>
            Returns a logarithmic list of values from minimum power of 2 to the maximum power of 2 using the provided iteration size.
            </summary>
            <param name="minPower">The minimum power of 2</param>
            <param name="maxPower">The maximum power of 2</param>
            <param name="iteration">The iteration size to use in powers</param>
            <returns></returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.ParameterSelection.Grid(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Parameter,System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  The default ranges of C and Gamma values are used.  Use this method if there is no validation data available, and it will
            divide it 5 times to allow 5-fold validation (training on 4/5 and validating on 1/5, 5 times).
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="outputFile">Output file for the parameter results.</param>
            <param name="C">The optimal C value will be put into this variable</param>
            <param name="Gamma">The optimal Gamma value will be put into this variable</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.ParameterSelection.Grid(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Parameter,System.Collections.Generic.List{System.Double},System.Collections.Generic.List{System.Double},System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  Use this method if there is no validation data available, and it will
            divide it 5 times to allow 5-fold validation (training on 4/5 and validating on 1/5, 5 times).
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="CValues">The set of C values to use</param>
            <param name="GammaValues">The set of Gamma values to use</param>
            <param name="outputFile">Output file for the parameter results.</param>
            <param name="C">The optimal C value will be put into this variable</param>
            <param name="Gamma">The optimal Gamma value will be put into this variable</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.ParameterSelection.Grid(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Parameter,System.Collections.Generic.List{System.Double},System.Collections.Generic.List{System.Double},System.String,System.Int32,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  Use this method if validation data isn't available, as it will
            divide the training data and train on a portion of it and test on the rest.
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="CValues">The set of C values to use</param>
            <param name="GammaValues">The set of Gamma values to use</param>
            <param name="outputFile">Output file for the parameter results.</param>
            <param name="nrfold">The number of times the data should be divided for validation</param>
            <param name="C">The optimal C value will be placed in this variable</param>
            <param name="Gamma">The optimal Gamma value will be placed in this variable</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.ParameterSelection.Grid(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Parameter,System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.  Uses the default values of C and Gamma.
            </summary>
            <param name="problem">The training data</param>
            <param name="validation">The validation data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="outputFile">The output file for the parameter results</param>
            <param name="C">The optimal C value will be placed in this variable</param>
            <param name="Gamma">The optimal Gamma value will be placed in this variable</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.ParameterSelection.Grid(Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Problem,Studio.Algorithm.SVM.Parameter,System.Collections.Generic.List{System.Double},System.Collections.Generic.List{System.Double},System.String,System.Double@,System.Double@)">
            <summary>
            Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
            combination which performed best.
            </summary>
            <param name="problem">The training data</param>
            <param name="validation">The validation data</param>
            <param name="parameters">The parameters to use when optimizing</param>
            <param name="CValues">The C values to use</param>
            <param name="GammaValues">The Gamma values to use</param>
            <param name="outputFile">The output file for the parameter results</param>
            <param name="C">The optimal C value will be placed in this variable</param>
            <param name="Gamma">The optimal Gamma value will be placed in this variable</param>
        </member>
        <member name="T:Studio.Algorithm.SVM.SvmType">
            <summary>
            Contains all of the types of SVM this library can model.
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.SvmType.C_SVC">
            <summary>
            C-SVC.
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.SvmType.NU_SVC">
            <summary>
            nu-SVC.
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.SvmType.ONE_CLASS">
            <summary>
            one-class SVM
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.SvmType.EPSILON_SVR">
            <summary>
            epsilon-SVR
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.SvmType.NU_SVR">
            <summary>
            nu-SVR
            </summary>
        </member>
        <member name="T:Studio.Algorithm.SVM.KernelType">
            <summary>
            Contains the various kernel types this library can use.
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.KernelType.LINEAR">
            <summary>
            Linear: u'*v
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.KernelType.POLY">
            <summary>
            Polynomial: (gamma*u'*v + coef0)^degree
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.KernelType.RBF">
            <summary>
            Radial basis function: exp(-gamma*|u-v|^2)
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.KernelType.SIGMOID">
            <summary>
            Sigmoid: tanh(gamma*u'*v + coef0)
            </summary>
        </member>
        <member name="F:Studio.Algorithm.SVM.KernelType.PRECOMPUTED">
            <summary>
            Precomputed kernel
            </summary>
        </member>
        <member name="T:Studio.Algorithm.SVM.Parameter">
            <summary>
            This class contains the various parameters which can affect the way in which an SVM
            is learned.  Unless you know what you are doing, chances are you are best off using
            the default values.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Parameter.#ctor">
            <summary>
            Default Constructor.  Gives good default values to all parameters.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Parameter.Clone">
            <summary>
            Creates a memberwise clone of this parameters object.
            </summary>
            <returns>The clone (as type Parameter)</returns>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.SvmType">
            <summary>
            Type of SVM (default C-SVC)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.KernelType">
            <summary>
            Type of kernel function (default Polynomial)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.Degree">
            <summary>
            Degree in kernel function (default 3).
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.Gamma">
            <summary>
            Gamma in kernel function (default 1/k)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.Coefficient0">
            <summary>
            Zeroeth coefficient in kernel function (default 0)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.CacheSize">
            <summary>
            Cache memory size in MB (default 100)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.EPS">
            <summary>
            Tolerance of termination criterion (default 0.001)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.C">
            <summary>
            The parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.WeightCount">
            <summary>
            Number of weights.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.WeightLabels">
            <summary>
            Array of indicies corresponding to the Weights array (for C-SVC)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.Weights">
            <summary>
            The parameter C of class i to weight*C in C-SVC (default 1)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.Nu">
            <summary>
            The parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.P">
            <summary>
            The epsilon in loss function of epsilon-SVR (default 0.1)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.Shrinking">
            <summary>
            Whether to use the shrinking heuristics, (default True)
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Parameter.Probability">
            <summary>
            Whether to train an SVC or SVR model for probability estimates, (default False)
            </summary>
        </member>
        <member name="T:Studio.Algorithm.SVM.Model">
            <summary>
            Encapsulates an SVM Model.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Model.Read(System.String)">
            <summary>
            Reads a Model from the provided file.
            </summary>
            <param name="filename">The name of the file containing the Model</param>
            <returns>the Model</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Model.Read(System.IO.Stream)">
            <summary>
            Reads a Model from the provided stream.
            </summary>
            <param name="stream">The stream from which to read the Model.</param>
            <returns>the Model</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Model.Write(System.String,Studio.Algorithm.SVM.Model)">
            <summary>
            Writes a model to the provided filename.  This will overwrite any previous data in the file.
            </summary>
            <param name="filename">The desired file</param>
            <param name="model">The Model to write</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.Model.Write(System.IO.Stream,Studio.Algorithm.SVM.Model)">
            <summary>
            Writes a model to the provided stream.
            </summary>
            <param name="stream">The output stream</param>
            <param name="model">The model to write</param>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.Parameter">
            <summary>
            Parameter object.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.NumberOfClasses">
            <summary>
            Number of classes in the model.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.SupportVectorCount">
            <summary>
            Total number of support vectors.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.SupportVectors">
            <summary>
            The support vectors.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.SupportVectorCoefficients">
            <summary>
            The coefficients for the support vectors.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.Rho">
            <summary>
            Rho values.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.PairwiseProbabilityA">
            <summary>
            First pairwise probability.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.PairwiseProbabilityB">
            <summary>
            Second pairwise probability.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.ClassLabels">
            <summary>
            Class labels.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Model.NumberOfSVPerClass">
            <summary>
            Number of support vectors per class.
            </summary>
        </member>
        <member name="T:Studio.Algorithm.SVM.Problem">
            <summary>
            Encapsulates a problem, or set of vectors which must be classified.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Problem.#ctor(System.Int32,System.Double[],Studio.Algorithm.SVM.Node[][],System.Int32)">
            <summary>
            Constructor.
            </summary>
            <param name="count">Number of vectors</param>
            <param name="y">The class labels</param>
            <param name="x">Vector data.</param>
            <param name="maxIndex">Maximum index for a vector</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.Problem.#ctor">
            <summary>
            Empty Constructor.  Nothing is initialized.
            </summary>
        </member>
        <member name="M:Studio.Algorithm.SVM.Problem.Read(System.IO.Stream)">
            <summary>
            Reads a problem from a stream.
            </summary>
            <param name="stream">Stream to read from</param>
            <returns>The problem</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Problem.Write(System.IO.Stream,Studio.Algorithm.SVM.Problem)">
            <summary>
            Writes a problem to a stream.
            </summary>
            <param name="stream">The stream to write the problem to.</param>
            <param name="problem">The problem to write.</param>
        </member>
        <member name="M:Studio.Algorithm.SVM.Problem.Read(System.String)">
            <summary>
            Reads a Problem from a file.
            </summary>
            <param name="filename">The file to read from.</param>
            <returns>the Probem</returns>
        </member>
        <member name="M:Studio.Algorithm.SVM.Problem.Write(System.String,Studio.Algorithm.SVM.Problem)">
            <summary>
            Writes a problem to a file.   This will overwrite any previous data in the file.
            </summary>
            <param name="filename">The file to write to</param>
            <param name="problem">The problem to write</param>
        </member>
        <member name="P:Studio.Algorithm.SVM.Problem.Count">
            <summary>
            Number of vectors.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Problem.Y">
            <summary>
            Class labels.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Problem.X">
            <summary>
            Vector data.
            </summary>
        </member>
        <member name="P:Studio.Algorithm.SVM.Problem.MaxIndex">
            <summary>
            Maximum index for a vector.
            </summary>
        </member>
    </members>
</doc>
